# An Improved HRNetV2-Based Semantic Segmentation Algorithm for Pipe Corrosion Detection in Smart City Drainage Networks

## TL;DR

This paper introduces a new AI method to detect pipe corrosion in smart city drainage systems using improved image analysis.

## Contribution

The novel integration of CBAM and LitePPM with HRNetV2 improves corrosion detection accuracy in drainage pipe images.

## Key findings

- The model achieved 95.92% mean Intersection over Union for corrosion segmentation.
- It demonstrated 97.01% recall and 98.54% overall accuracy in real-world tests.

## Abstract

Urban drainage pipelines are essential components of smart city infrastructure, supporting the safe and sustainable operation of underground systems. However, internal corrosion in pipelines poses significant risks to structural stability and public safety. In this study, we propose an enhanced semantic segmentation framework based on High-Resolution Network Version 2 (HRNetV2) to accurately identify corroded regions in Traditional closed-circuit television (CCTV) images. The proposed method integrates a Convolutional Block Attention Module (CBAM) to strengthen the feature representation of corrosion patterns and introduces a Lightweight Pyramid Pooling Module (LitePPM) to improve multi-scale context modeling. By preserving high-resolution details through HRNetV2’s parallel architecture, the model achieves precise and robust segmentation performance. Experiments on a real-world corrosion dataset show that our approach attains a mean Intersection over Union (mIoU) of 95.92 ± 0.03%, Recall of 97.01 ± 0.02%, and an overall Accuracy of 98.54%. These results demonstrate the method’s effectiveness in supporting intelligent infrastructure inspection and provide technical insights for advancing automated maintenance systems in smart cities.